What are the features of targeted or system-wide initiatives that affect diversity in health professions trainees? A BEME systematic review: BEME Guide No. 50
Bibliographic record
Abstract
BACKGROUND/PURPOSE: There is interest to increase diversity among health professions trainees. This study aims to determine the features/effects of interventions to promote recruitment/admission of under-represented minority (URM) students to health professions programs. METHODOLOGY: This registered BEME review applied systematic methods to: title/full-text inclusion review, data extraction, and quality assessment (QA). Included studies reported outcomes for interventions designed to increase diversity of health professions education (HPE) programs' recruitment and admissions. RESULTS: Of 7225 studies identified 86 met inclusion criteria. Interventions addressed: admissions (34%), enrichment (19%), outreach (15%), curriculum (3%), and mixed (29%). They were mostly single center (76%), from the United States (81%), in medicine (45%) or dentistry (22%). URM definition was stated in only 24%. The dimension most commonly considered was ethnicity/race (88%). The majority of studies (81%) found positive effects. Heterogeneity precluded meta-analysis. Qualitative analysis identified key features: admissions studies points systems and altered weightings; enrichment studies highlighted academic, application and exam preparation, and workplace exposure. DISCUSSION/CONCLUSIONS: Several intervention types may increase diversity. Limited applicant pools were a rate-limiting feature, suggesting efforts earlier in the continuum are needed to broaden applicant pools. There is a need to examine underlying cultural and external pressures that limit programs' acceptance of initiatives to increase diversity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".